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Article
Publication date: 27 July 2018

Weidong Wang, Yongqing Dong, Renfu Luo, Yunli Bai and Linxiu Zhang

The purpose of this paper is to examine the role of education in the labor market and to understand how returns to education change over time in rural China.

Abstract

Purpose

The purpose of this paper is to examine the role of education in the labor market and to understand how returns to education change over time in rural China.

Design/methodology/approach

Using nationally representative survey data from 2004 to 2015, this study provides insights on wage determination in the labor market and examines how the returns to education in rural China differ with time and educational endowment. This study applies ordinary least squares estimation and the Heckman selection model to estimate the returns to education.

Findings

The returns to education decreased during the observed years from more than 6 percent in 2004 to only about 3 percent in 2011, rising to nearly 4 percent in 2015. The overall trend is robust and observed within groups defined by education. Additionally, the returns to education vary greatly with educational endowment. Tertiary education has always maintained a high rate of returns at nearly 10 percent, while returns to senior high school education and below have gradually diminished.

Originality/value

The authors believe that the results will not only enrich studies on the returns to education in rural China, but also provide a basis for diagnosing the changes of rural labor market in the early twenty-first century.

Details

China Agricultural Economic Review, vol. 11 no. 1
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 8 May 2018

Linxiu Zhang, Yongqing Dong, Chengfang Liu and Yunli Bai

The purpose of this paper is to evaluate the trend of off-farm employment in rural China over the past four decades since the reform and opening-up.

Abstract

Purpose

The purpose of this paper is to evaluate the trend of off-farm employment in rural China over the past four decades since the reform and opening-up.

Design/methodology/approach

Using two sets of panel survey data, the China National Rural Survey conducted in 2000 and 2008, and the China Rural Development Survey conducted in 2005, 2008, 2012 and 2016, this study offers a re-visit of China’s off-farm employment to give us the latest information about its evolution and whether rural labor markets have developed in a way that will allow them to facilitate the transformation of China’s economy more effectively. The evolution of off-farm employment is further examined through decomposition of types, destinations, industries, and population sub-groups as well as the change in the wage rate.

Findings

The data show the rapid increase in rural labor activities over the whole study period. Most notably, the authors findnd that a rapid rise in off-farm employment has continued even until after 2008 and into the mid-2010s, which is a time when some feared that macroeconomic conditions might keep rural residents on the farm or drive them back to the farm. In the disaggregation of labor market trends, the authors show that labor markets are acting consistently with an economy that is in transition from being dominated by agriculture to being dominated by other forms of production and with a population that is consistently becoming more urban.

Originality/value

The authors believe that the results will contribute positively to the exploration of answers to the question whether or not rural labor markets have developed in a way that will allow them to facilitate the transformation of China’s economy more effectively over the last four decades.

Details

China Agricultural Economic Review, vol. 10 no. 2
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 9 September 2022

Lucie Maruejols, Hanjie Wang, Qiran Zhao, Yunli Bai and Linxiu Zhang

Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require…

Abstract

Purpose

Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.

Design/methodology/approach

Households report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.

Findings

The random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.

Practical implications

To reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.

Originality/value

For the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.

Details

China Agricultural Economic Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 15 February 2022

Yunli Bai, Weidong Wang and Linxiu Zhang

The purpose of this paper is to examine the occupational specialization in rural labor market by analyzing the nature of part-time farming in rural China and estimating…

Abstract

Purpose

The purpose of this paper is to examine the occupational specialization in rural labor market by analyzing the nature of part-time farming in rural China and estimating the impact of off-farm experience on the individual’s persistence and exit of part-time farming as well as its heterogeneity.

Design/methodology/approach

Using the panel data collected in 100 villages and 2,000 households across five provinces in 2008, 2012 and 2016, this study provides insights on the nature of part-time farming in rural labor market and find the impact and mechanism of off-farm employment experience on exiting part-time farming by adopting event history analysis.

Findings

Part-time farming is a stable long-run occupation in rural labor market of China from 2008 to 2015. Off-farm employment experience generally has positive effects on long-term part-time farming and the probability of exiting part-time farming. It significantly promotes female to exit part-time farming.

Originality/value

Based on the two-sector model, this study builds a conceptual framework of off-farm experience and occupational specialization and sets a theoretical basis of hazard model when using event history analysis. This study contributes to identify the impact of off-farm experience on persistence and exiting part-time farming in recent years. The empirical findings support the policy of promoting off-farm employment to improve occupational specialization.

Details

China Agricultural Economic Review, vol. 14 no. 3
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 9 March 2021

Yunli Bai, Tianhao Zhou, Zhiyuan Ma and Linxiu Zhang

The purpose of this paper is to examine the role of infrastructure on the income growth and poverty reduction of rural household in China by estimating the impact of road…

Abstract

Purpose

The purpose of this paper is to examine the role of infrastructure on the income growth and poverty reduction of rural household in China by estimating the impact of road accessibility on the extent of household off-farm employment and its heterogeneous effects among the groups with different income level and earning capacity.

Design/methodology/approach

Using nationally representative panel data collected in 100 villages about 2000 households across five provinces in 2005, 2008, 2012, 2016 and 2019. This study adopts Tobit model with panel data, zero-inflated Poisson model and static nonbalanced panel model to yield consistent results.

Findings

We find that road accessibility generally has no effect on the number of off-farm laborers and duration of off-farm employment. However, road accessibility is not beneficial for the households in the low-income villages or with low educational attainment, but it benefits the households in the high-income villages by promoting local off-farm employment or with high educational attainment by increasing the duration of migrant off-farm employment.

Originality/value

This study identifies the heterogeneous effects of road accessibility on the extent of off-farm employment among rural households, which narrows the research gap and enriches the literature. The empirical findings imply that road accessibility widens the gap between rich and poor in off-farm employment, which is of great important to the alleviation of relative poverty after 2020 in China.

Details

China Agricultural Economic Review, vol. 13 no. 3
Type: Research Article
ISSN: 1756-137X

Keywords

Abstract

Details

China Agricultural Economic Review, vol. 10 no. 1
Type: Research Article
ISSN: 1756-137X

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